Search Results (2,298)

Background: Electroencephalography (EEG) offers millisecond-precision measurement of neural oscillations underlying human cognition and emotion. Despite extensive research, systematic frameworks mapping EEG metrics to psychological constructs remain fragmented. Objective: This interdisciplinary scoping review synthesizes current knowledge linking EEG signatures to affective and cognitive models from a neuroscience perspective. Methods: We examined empirical studies employing diverse EEG methodologies, from traditional spectral analysis to deep learning approaches, across laboratory and naturalistic settings. Results: Affective states manifest through distinct frequency-specific patterns: frontal alpha asymmetry (8–13 Hz) reliably indexes emotional valence with 75–85% classification accuracy, while arousal correlates with widespread beta/gamma power changes. Cognitive processes show characteristic signatures: frontal–midline theta (4–8 Hz) increases linearly with working memory load, alpha suppression marks attentional engagement, and theta/beta ratios provide robust cognitive load indices. Machine learning approaches achieve 85–98% accuracy for subject identification and 70–95% for state classification. However, significant challenges persist: spatial resolution remains limited (2–3 cm), inter-individual variability is substantial (alpha peak frequency: 7–14 Hz range), and overlapping signatures compromise diagnostic specificity across neuropsychiatric conditions. Evidence strongly supports integrated rather than segregated processing, with cross-frequency coupling mechanisms coordinating affective–cognitive interactions. Conclusions: While EEG-based assessment of mental states shows considerable promise for clinical diagnosis, brain–computer interfaces, and adaptive technologies, realizing this potential requires addressing technical limitations, standardizing methodologies, and establishing ethical frameworks for neural data privacy. Progress demands convergent approaches combining technological innovation with theoretical sophistication and ethical consideration. Full article

Following natural disasters, children and adolescents are particularly vulnerable to the onset and persistence of post-traumatic stress symptoms, which can significantly affect developmental trajectories and mental health. Although PTSD networks have been extensively studied in adults, less is known about youth, and no previous studies have examined how PTSD clusters relate to social, emotional, and behavioral difficulties (SEBD). This study applied network analysis to examine how PTSD clusters relate to psychosocial problems in a large sample of trauma-exposed youth. A total of 635 Italian children and adolescents (M_age = 11.19 years, SD = 1.43; 51.5% male), exposed to the 2012 Emilia-Romagna earthquake, completed the UCLA PTSD-RI and the Strengths and Difficulties Questionnaire. Network estimation and centrality indices were computed for the overall sample. Network Comparison Tests assessed differences by gender, age group, and proximity to the epicenter. Emotional problems and Increased Arousal emerged as the most central nodes, whereas Peer Problems were consistently peripheral. Gender differences were significant, whereas no differences were detected by age; moreover, youth living closer to the epicenter exhibited a more densely connected network. Despite its limitations, the study identifies co-occurrence patterns between PTSD clusters and specific SEBD, outlining clinical implications that warrant further investigation. Full article

Background/Objectives: Anxiety and depression are prevalent global health concerns, especially prominent in vulnerable groups such as older adults, individuals with chronic health conditions (e.g., neurodegeneration and cancer), and those from low socioeconomic backgrounds. Digital interventions, including computerized cognitive training (CCT), show promise in addressing emotional dysfunctions in a more accessible and cost-effective manner. The CREATE platform aims to enhance Emotion Regulation (ER) through targeted Working Memory (WM) training, aesthetic engagement, and creativity, while accounting for dopamine activity via spontaneous Eye Blink Rate (sEBR). The purpose of the present study is to evaluate the platform’s feasibility and validity through a single pilot trial. Methods: The study enrolled twenty-seven healthy adults (aged 21–44) who completed standardized self-report questionnaires on sleep quality and ER. They were also enrolled in sEBR recordings and performed a CCT-adapted Corsi block-tapping task and an aesthetic art evaluation. Affective textual narratives and valence/arousal ratings were also collected. Participants were divided into “Good Sleepers” and “Poor Sleepers”. The platform evaluation enrolled a multi-modal pipeline including correlations and regression analysis of intervention metrics, sentiment analysis, and group comparisons. Results: WM task performance correlated positively with global ER and Cognitive Reappraisal scores. Post-training sEBR was significantly associated with ER, and lower sleep efficiency negatively impacted changes in dopamine activity (sEBR Diff). Dopamine activity of “Good Sleepers”, as indicated by sEBR, reached the high levels of the “Poor Sleepers” group after the training, suggesting a dopamine boost caused by the CREATE platform for those with quality sleep. Creativity and emotional expression, as indicated by sentiment analysis, were related to sleep quality. Conclusions: The CREATE platform shows promise in enhancing ER through multi-modal digital engagement, integrating cognitive training, art, and creativity. The findings support the inclusion of sleep and dopamine markers in intervention evaluation. Further studies with larger samples and clinical cohorts are warranted to establish efficacy and generalizability, as the present one was not powered to test the effectiveness of our training platform but was designed to assess its feasibility and validity instead. Full article

Background: The assessment of emotion recognition holds growing significance in research on the brain–computer interface and human–computer interaction. Among diverse physiological signals, electroencephalography (EEG) occupies a pivotal position in affective computing due to its exceptional temporal resolution and non-invasive acquisition. However, EEG signals are inherently complex, characterized by substantial noise contamination and high variability, posing considerable challenges to accurate assessment. Methods: To tackle these challenges, we propose a Triple Attention Network (TANet), a triple-attention EEG emotion recognition framework that integrates Conformer, Convolutional Block Attention Module (CBAM), and Mutual Cross-Modal Attention (MCA). The Conformer component captures temporal feature dependencies, CBAM refines spatial channel representations, and MCA performs cross-modal fusion of differential entropy and power spectral density features. Results: We evaluated TANet on two benchmark EEG emotion datasets, DEAP and SEED. On SEED, using a subject-specific cross-validation protocol, the model reached an average accuracy of 98.51 ± 1.40%. On DEAP, we deliberately adopted a segment-level splitting paradigm—in line with influential state-of-the-art methods—to ensure a direct and fair comparison of model architecture under an identical evaluation protocol. This approach, designed specifically to assess fine-grained within-trial pattern discrimination rather than cross-subject generalization, yielded accuracies of 99.69 ± 0.15% and 99.67 ± 0.13% for the valence and arousal dimensions, respectively. Compared with existing benchmark approaches under similar evaluation protocols, TANet delivers substantially better results, underscoring the strong complementary effects of its attention mechanisms in improving EEG-based emotion recognition performance. Conclusions: This work provides both theoretical insights into multi-dimensional attention for physiological signal processing and practical guidance for developing high-performance, robust EEG emotion assessment systems. Full article

Perfluorooctanesulfonate (PFOS) is a typical persistent organic pollutant, which presents a significant risk to the ecosystem and human health. Therefore, the development of a highly sensitive and effective detection technique for PFOS has aroused wide concern. In this study, for the mesoporous metal–organic frameworks (MOFs), Cr-MIL-101 were used as the precursor. And the poly(3,4-ethylenedioxythiophene) (PEDOT) using as molecularly imprinted polymers (MIPs) was loaded on Cr-MIL-101 to form a core–shell structure. The obtained Cr-MIL-101@PEDOT/MIP composites integrate the high specific surface area of Cr-MIL-101 and the specific recognition capability of PEDOT/MIP. The glassy carbon electrode (GCE) interface modified by them can specifically adsorb PFOS through electrostatic interactions, coordination by Cr metal nodes, hydrophobic interaction, and hydrogen bonding, etc. The adsorbed PFOS molecules could block the active sites at the electrode interface, causing the current decay of the redox probe. Following the quantitative analysis of peak current decay values using the Langmuir model and the Freundlich–Langmuir model, a wide detection range (0.1–200 nM) and a low detection limit (0.025 nM) were obtained. Characterization techniques including scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), X-ray photoelectron spectroscopy (XPS), and electrochemical methods were employed to validate the fabrication of the composites. Moreover, Cr-MIL-101@PEDOT/MIP/GCE showed satisfactory stability, repeatability, and selectivity, providing an effective method for the detection of PFOS in practical samples, showing a wide prospective application. Full article

Background: The interaction between the endocannabinoid system (ECS), specifically the CB1 and CB2 cannabinoid receptors, and neuropathy has aroused great research interest due to the possible implications for treatment. Complications following type 1 diabetes, due to impaired glucose metabolism and chronic inflammation, may benefit from targeted therapeutic strategies involving the ECS. This study explores the effects of photobiomodulation therapy (PBMT) on streptozotocin (STZ)-induced diabetic peripheral neuropathy (DPN) in rats. The study assessed body mass, hyperglycemia, mechanical hyperalgesia, and the influence of PBMT on these conditions over four weeks. Results showed that while PBMT did not alter the metabolic aspects of type I diabetes, it significantly reduced mechanical hyperalgesia compared to untreated diabetic neuropathic rats. Notably, cannabinoid receptor antagonists for CB1 and CB2 elicited a transient reversal of this antihyperalgesic effect, indicating a potential role of these receptors in PBMT’s mechanism. However, CB2 modulation was not statistically significant, whereas changes in CB1 receptor expression were observed in the dorsal root ganglia, suggesting its involvement in PBMT’s effects. These findings highlight the importance of CB1 and CB2 receptors in DPN and suggest that PBMT may offer a therapeutic benefit by mitigating mechanical hyperalgesia. Further investigation into cannabinoid receptor dynamics in diabetes could help in new therapeutic strategies for managing diabetic complications. Full article

Electroencephalography is a widely used method for emotion recognition. However, it requires specialized equipment, leading to high costs. Additionally, attaching devices to the body during such procedures may cause physical and psychological stress to participants. These issues are addressed in this study by focusing on physiological signals that are noninvasive and contact-free, and a generalized method for estimating emotions is developed. Specifically, the facial skin temperature and eye-opening degree of participants captured via infrared thermography and visible cameras are utilized, and emotional states are estimated while Japanese older adults view digital content. Emotional responses while viewing digital content are often subtle and dynamic. Additionally, various emotions occur during such situations, both positive and negative. Fluctuations in facial skin temperature and eye-opening degree reflect activities in the autonomic nervous system. In particular, expressing emotions through facial expressions is difficult for older adults; as such, emotional estimation using such ecological information is required. Our study results demonstrated that focusing on skin temperature changes and eye movements during emotional arousal and non-arousal using bidirectional long short-term memory yields an F1 score of 92.21%. The findings of this study can enhance emotion recognition in digital content, improving user experience and the evaluation of digital content. Full article

Background: Sexual dysfunction (SD) is a common yet under-recognized consequence of traumatic brain injury (TBI), with significant implications for physical health, psychological well-being, interpersonal relationships and social reintegration. Although TBI research has largely focused on cognitive, motor and behavioral outcomes, the impact of SD remains insufficiently addressed in both clinical practice and rehabilitation programs. Objectives: This review aims to synthesize current evidence on the prevalence, mechanisms and management of SD following TBI, while emphasizing the importance of gender-sensitive and multidisciplinary approaches to care. Methods: A narrative review was conducted by searching PubMed, Scopus and Web of Science for English-language articles published between 2000 and 2025 using combinations of the following keywords: traumatic brain injury, sexual dysfunction, neuroendocrine dysfunction, psychological sequelae and rehabilitation. Priority was given to peer-reviewed clinical studies, systematic reviews and expert consensus guidelines that addressed neurological, endocrine, cognitive, psychological and social aspects of SD in TBI survivors. Exclusion criteria included case reports with insufficient clinical detail and non-peer-reviewed sources. Articles were screened for relevance to both pathophysiological mechanisms and therapeutic strategies. Results: The etiology of post-TBI SD is multifactorial, involving direct neurological injury, hypothalamic–pituitary dysfunction, emotional and cognitive impairments, as well as psychological challenges such as stigma and relationship strain. Men and women may present distinct symptom profiles; for instance, men more frequently report erectile dysfunction and hypogonadism, whereas women more commonly experience challenges with arousal, lubrication and psychological stress. Effective interventions include pharmacotherapy, hormone replacement therapy, psychotherapy and rehabilitative approaches designed to restore intimacy and quality of life. Optimal outcomes are achieved through multidisciplinary collaboration among neurology, endocrinology, psychiatry, psychology and rehabilitation medicine. Conclusions: Sexual dysfunction should be recognized as a critical component of TBI sequelae rather than a secondary concern. Routine screening, gender-sensitive assessment and the integration of individualized, multidisciplinary care pathways are essential to improving patient outcomes. Advancing clinical awareness and standardization in this area holds the potential to significantly enhance the holistic recovery and reintegration of TBI survivors. Full article

An individual’s ability to process flickering light is expressed by critical flicker fusion frequency (CFFF), tested with the flicker test. CFFF is used to assess visual processing, arousal, and cognitive functioning, among other things, although it is unclear how it reflects these processes. Due to possible differences between CFFF values obtained in trials with increasing and decreasing frequency, it also remains questionable to use only averaged CFFF values in research. The main objective of the present study was to assess how CFFF is related to cognitive functions (attention, short-term and working memory, and executive functions), and psychomotor speed. The research objectives also included assessing the stability of CFFF and its variability with age and comparing CFFF between men and women. Thirty-six participants (17 women and 19 men) completed computerized cognitive tests (Simon and flanker tasks, the Corsi block-tapping task, and the digit span task) three times, along with the flicker test. We found that CFFF scores were stable across sessions but differed between fusion and flicker thresholds, with age significantly correlating only with the fusion frequency. Given that, we suggest that future studies analyze not only the averaged CFFF, but also examine flicker and fusion thresholds separately to better understand their distinct contributions. Our results also revealed generally weak correlations between CFFF and neuropsychological test scores, with significant associations found only in women, suggesting that CFFF may not be a reliable indicator of cognitive functioning. Full article

This feasibility study explored the integration of physiological monitoring into a virtual reality (VR) intervention for pediatric pain management. The goal of this study is to identify a feasible strategy for collecting physiologic data in the context of a VR intervention currently being developed for youth with chronic pain. We assess the potential of Cognitive Load (CL)—derived from heart rate and pupillometry/eye-tracking data—as a marker of arousal and user engagement in a VR simulation to promote school functioning in youth with chronic pain. The HP Reverb G2 Omnicept headset and Polar H10 heart-rate sensor were utilized. The Child Presence Questionnaire (CPQ) assessed participants’ self-reported immersion and engagement. Data collection focused on feasibility and utility of physiologic data in assessing arousal and correlations with self-reported experience. Nine participants engaged in the simulation, with eight yielding complete data. The simulation and headset were well tolerated. CPQ Transportation subscale showed trend-level correlation with mean CL. Due to small sample and feasibility focus, individual-level results were examined. Combining multiple physiologic markers into a construct like CL is intriguing, but data interpretability was limited. Pupillometry and related metrics show promise as feasible markers of engagement and arousal for VR-based intervention but require appropriate expertise to fully interpret. The study found that integration of physiologic monitoring is feasible, but further work is needed to standardize metrics and identify the most useful and user-friendly markers. Full article

Since the earliest investigations into the impact of sleep-related breathing disorders on cardiovascular risk, the association between sleep–wake disorders and major cerebrovascular events has been increasingly and robustly established. Recent international joint statements—endorsed by leading scientific societies (e.g., American Heart Association, American Stroke Association, European Academy of Neurology, European Stroke Organization, European Sleep Research Society, and European Respiratory Society)—represent a milestone in stroke prevention and rehabilitation by formally recognizing sleep disorders as both risk factors for ischemic stroke and determinants of poor short- and long-term outcomes. Nevertheless, despite these strong epidemiological and mechanistic associations, the therapeutic evidence supporting sleep–wake interventions (e.g., positive airway pressure therapy, GABA-receptor agonists, melatonin) for stroke prevention remains limited and requires further validation through well-designed clinical trials. In this perspective article, we review recent advances in understanding the bidirectional relationship between sleep disorders and stroke, discuss the proposed pathophysiological mechanisms underpinning this complex interplay—with particular emphasis on arousal-related activation of the autonomic nervous system—and provide a critical appraisal of current research directions and future perspectives. Finally, we underscore the need for closer collaboration between sleep and stroke specialists to bridge existing knowledge gaps and optimize patient care. Full article

Objectives: Ammonia inhalants (AIs) are commonly used in competition with the assumption that they will increase arousal and reduce the detrimental effects of mental fatigue on performance. However, as the effect of AIs on mental fatigue is unclear, this study investigated (1) whether mental-fatigue-related changes in mood states are associated with reductions in maximal lower-body force production and (2) whether AIs reduce any mental-fatigue-induced changes in performance. Methods: In a randomized, crossover designed study, nine resistance trained males completed two trials, with and without AIs. Profile of mood states, isometric midthigh pull force, and electromyography were measured before and after completion of a 75 min AX-continuous performance test (AX-CPT). For AI trials, AIs were used prior to post-AX-CPT IMTPs. Results: The AX-CPT significantly increased all negative mood subscales, while decreasing vigor (all p < 0.05), resulting in an increase in total mood disturbance (pre-AX-CPT: 27.1 ± 3.17 vs. post-AX-CPT: 64.49 ± 4.01; p = 0.005). Additionally, compared to baseline, force was reduced immediately (1699 ± 345 vs. 1521 ± 324 N; p = 0.009), but not five minutes post-AX-CPT (p = 0.328). Electromyography did not change over time, and no differences between treatments were evident for any of the measures. Conclusions: Mental fatigue, and related mood disturbance, has the potential to acutely reduce lower-body, maximal force. This finding may have implications for athletes competing in strength sports where mental focus, arousal and maximal force production determine optimal performance. However, AIs offer no benefit to alleviating the detrimental effects of mental fatigue. Full article

Immersive virtual reality (IVR) has demonstrated significant potential in educational contexts. Nonetheless, prior IVR implementations have primarily focused on visual and auditory simulations, neglecting olfaction, which has limited immersive learning. To address this gap, we conducted an experimental study involving 64 students to examine the impact of integrating olfactory stimulus into IVR systems for fire safety training. Participants were randomly assigned to the control group (without olfactory stimulus, n = 32) or the experimental group (with olfactory stimulus, n = 32). The results indicated that the integration of olfactory stimulus significantly promoted high-arousal positive emotions, increased sense of presence, and reduced cognitive load—although it did not significantly improve learning performance. Thematic analysis further revealed that the incorporation of olfactory stimulus provided learners with an immersive learning experience. Moreover, this IVR system with olfactory stimulus had a high quality of experience. These findings have significant implications for the practice of learning in IVR and multisensory learning theory. Full article

A core challenge in multimodal emotion recognition lies in the precise capture of the inherent multimodal interactive nature of human emotions. Addressing the limitation of existing methods, which often process visual signals (facial expressions) and physiological signals (EEG, ECG, EOG, and GSR) in isolation and thus fail to exploit their complementary strengths effectively, this paper presents a new multimodal emotion recognition framework called the Gated Biological Visual Network (GBV-Net). This framework enhances emotion recognition accuracy through deep synergistic fusion of facial expressions and physiological signals. GBV-Net integrates three core modules: (1) a facial feature extractor based on a modified ConvNeXt V2 architecture incorporating lightweight Transformers, specifically designed to capture subtle spatio-temporal dynamics in facial expressions; (2) a hybrid physiological feature extractor combining 1D convolutions, Temporal Convolutional Networks (TCNs), and convolutional self-attention mechanisms, adept at modeling local patterns and long-range temporal dependencies in physiological signals; and (3) an enhanced gated attention fusion module capable of adaptively learning inter-modal weights to achieve dynamic, synergistic integration at the feature level. A thorough investigation of the publicly accessible DEAP and MAHNOB-HCI datasets reveals that GBV-Net surpasses contemporary methods. Specifically, on the DEAP dataset, the model attained classification accuracies of 95.10% for Valence and 95.65% for Arousal, with F1-scores of 95.52% and 96.35%, respectively. On MAHNOB-HCI, the accuracies achieved were 97.28% for Valence and 97.73% for Arousal, with F1-scores of 97.50% and 97.74%, respectively. These experimental findings substantiate that GBV-Net effectively captures deep-level interactive information between multimodal signals, thereby improving emotion recognition accuracy. Full article

Background/Objectives: Movement during attention-demanding tasks may help compensate for cortical under-arousal in pediatric ADHD patients. However, the influence of medication during movement is unknown. This study assessed the impact of concurrent movement during executive functioning tasks on dorsolateral prefrontal cortex (DLPFC) activation and inhibitory control, with a particular focus on the influence of medication status. Methods: Twenty-six children with ADHD (15 medicated; 11 unmedicated) and 24 children without ADHD performed a Stroop task under two conditions: while remaining seated (Stationary condition) and while pedalling on a desk cycle (Movement condition). Functional near-infrared spectroscopy (fNIRS) was used to measure changes in oxygenated and deoxygenated hemoglobin levels in the left DLPFC. Results: Sixty-four percent of unmedicated children with ADHD showed greater left DLPFC activity while desk-cycling compared to remaining stationary. Only 37% of medicated children with ADHD showed the same pattern, with 63% showing greater left DLPFC activation when remaining stationary during executive functioning. Children without ADHD had similar DLPFC patterns as unmedicated ADHD children, with 65% showing increased activation during movement. Unmedicated ADHD children who were able to desk-cycle during the Stroop task had higher overall and incongruent accuracy scores; no Stroop differences were found between conditions for children with ADHD who were medicated or for controls. Conclusions: Medicated ADHD children did not benefit from physical activity during tasks requiring executive control, yet unmedicated ADHD children showed significantly greater DLPFC activation and inhibitory control when engaging in movement. If medication is not suitable for children with ADHD due to adverse side effects, movement during executive functioning may help mimic the benefit of medications and similarly support attention. Full article